As industries move toward multimedia rich working environments, usage of all forms of audio and visual content representations (radio broadcast transmissions, streaming video, audio canvas, visual summarization, etc.) becomes more frequent. Whether a user, content provider, or both, everybody searches for ways to optimally utilize such content. For example, one method that has much potential for creative uses is content identification. Enabling a user to identify content that the user is listening to or watching offers a content provider new possibilities for success.
In the field of broadcast monitoring and subsequent content identification, it is often desirable to identify as much audio content as possible while minimizing effort expended. In an example system, audio samples from a broadcast stream (such as a radio or television broadcast) are recorded and each audio sample is sent to an identification means, which returns an identity of the content of the audio sample. The recordation time of each audio sample can also be noted and a broadcast playlist can then be constructed that may list the audio tracks that were broadcast on each broadcast channel being monitored.
Existing monitoring systems may sample a broadcast stream periodically instead of continually and thus potentially under-sample the broadcast stream. In such an instance, content of a short duration may not be sampled at all and may be missed entirely. Alternatively, a monitoring system may over-sample a broadcast stream, which results in performing redundant samplings and content identifications, wasting computational effort. As a result, a method of optimizing sampling periods is desirable.